8 research outputs found
Reputation-based provider incentivisation for provenance provision
Knowledge of circumstances under which past service provisions have occurred enables clients to make more informed selection decisions regarding their future interaction partners. Service providers, however, may often be reluctant to release such circumstances due to the cost and effort required, or to protect their interests. In response, we introduce a reputation-based incentivisation framework, which motivates providers towards the desired behaviour of reporting circumstances via influencing two reputation-related factors: the weights of past provider interactions, which directly impact the provider’s reputation estimate, and the overall confidence in such estimate
The role and challenges of education for responsible AI
Artificial intelligence (AI) is impacting education in many different ways. From virtual
assistants for personalized education, to student or teacher tracking systems, the
potential benefits of AI for education often come with a discussion of its impact
on privacy and well-being. At the same time, the social transformation brought
about by AI requires reform of traditional education systems. This article discusses
what a responsible, trustworthy vision for AI is and how this relates to and affects
education
An intelligent system for personalized conference event recommendation and scheduling
Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0)</p
Reinforcement learning for trading dialogue agents in non-cooperative negotiations
Recent advances in automating Dialogue Management have been mainly made in
cooperative environments -where the dialogue system tries to help a human to meet
their goals. In non-cooperative environments though, such as competitive trading,
there is still much work to be done. The complexity of such an environment rises
as there is usually imperfect information about the interlocutors’ goals and states.
The thesis shows that non-cooperative dialogue agents are capable of learning how
to successfully negotiate in a variety of trading-game settings, using Reinforcement
Learning, and results are presented from testing the trained dialogue policies with
humans. The agents learned when and how to manipulate using dialogue, how to
judge the decisions of their rivals, how much information they should expose, as well
as how to effectively map the adversarial needs in order to predict and exploit their
actions. Initially the environment was a two-player trading game (“Taikun”). The
agent learned how to use explicit linguistic manipulation, even with risks of exposure
(detection) where severe penalties apply. A more complex opponent model for
adversaries was also implemented, where we modelled all trading dialogue moves as
implicitly manipulating the adversary’s opponent model, and we worked in a more
complex game (“Catan”). In that multi-agent environment we show that agents
can learn to be legitimately persuasive or deceitful. Agents which learned how to
manipulate opponents using dialogue are more successful than ones which do not
manipulate. We also demonstrate that trading dialogues are more successful when
the learning agent builds an estimate of the adversarial hidden goals and preferences.
Furthermore the thesis shows that policies trained in bilateral negotiations can be
very effective in multilateral ones (i.e. the 4-player version of Catan). The findings
suggest that it is possible to train non-cooperative dialogue agents which successfully
trade using linguistic manipulation. Such non-cooperative agents may have
important future applications, such as on automated debating, police investigation,
games, and education